This paper studies discriminant learning for guessing one of these demographic facets: the gender of an asker. In so doing, it capitalizes on a large-scale corpus automatically constructed from the integration of Yahoo! Search and Yahoo! Answer profiles. Then, this corpus is utilized for examining the impact of numerous features extracted from assorted sources: texts, demographics, meta-data, social interactions and web search. In brief, good non-linguistic gender indicators were age, industry and second-level question categories. If these are inaccessible, our outcomes indicate that models can still infer them, to some extent, from textual sources by means of semantic analysis and dependency relations. Overall, our best configuration reached an accuracy of 74.50%. (C) 2017 Elsevier Ltd. All rights reserved.